Classifying ECG signals

ABSTRACT

A method, including receiving a bipolar signal from a pair of electrodes in proximity to a myocardium of a human subject, and receiving a unipolar signal from a selected one of the pair of electrodes. The method further includes delineating a window of interest (WOI) for the unipolar and bipolar signals, within the WOI computing local unipolar minimum derivatives of the unipolar signal, and times of occurrence of the local unipolar minimum derivatives, and within the WOI computing bipolar derivatives of the bipolar signal at the times of occurrence. The method also includes evaluating ratios of the bipolar derivatives to the local unipolar minimum derivatives, and when the ratios are greater than a preset threshold ratio value, assigning the times of occurrence as times of activation of the myocardium, counting a number of the times of activation; and classifying the unipolar signal according to the number.

CROSS-REFERENCE TO RELATED APPLICATION(S)

This application is a divisional of U.S. patent application Ser. No.15/646,393, filed Jul. 11, 2017, now U.S. Pat. No. 11,058,342, whichclaims the benefit of U.S. Provisional Patent Application 62/373,465,filed Aug. 11, 2016, the content of both of which is incorporated hereinby reference. This application is related to U.S. patent applicationSer. No. 15/646,373, filed Jul. 11, 2017, titled “Annotation of aWavefront,” now U.S. Pat. No. 10,383,534.

FIELD OF THE INVENTION

This invention relates generally to electrocardiograph (ECG) signals,and specifically to a method for classifying the signals.

BACKGROUND OF THE INVENTION

Mapping and imaging of the electrical signals in the heart is typicallybased on combining local activation time (LAT), as indicated by acatheter's ECG signals, with the spatial position of the signals. Such amethod is used in the CARTO® 3 System, produced by Biosense Webster ofDiamond Bar, Calif.

Documents incorporated by reference in the present patent applicationare to be considered an integral part of the application except that, tothe extent that any terms are defined in these incorporated documents ina manner that conflicts with definitions made explicitly or implicitlyin the present specification, only the definitions in the presentspecification should be considered.

SUMMARY OF THE INVENTION

An embodiment of the present invention provides a method, includingreceiving a bipolar signal from a pair of electrodes in proximity to amyocardium of a human subject, and receiving a unipolar signal from aselected one of the pair of electrodes. The method also includesdelineating a window of interest (WOI) for the unipolar and bipolarsignals, and within the WOI computing local unipolar minimum derivativesof the unipolar signal, and times of occurrence of the local unipolarminimum derivatives.

The method further includes, within the WOI, computing bipolarderivatives of the bipolar signal at the times of occurrence, evaluatingratios of the bipolar derivatives to the local unipolar minimumderivatives, and when the ratios are greater than a preset thresholdratio value, assigning the times of occurrence as times of activation ofthe myocardium, counting a number of the times of activation, andclassifying the unipolar signal according to the number.

In a disclosed embodiment, when the bipolar derivatives are less than apreset bipolar derivative threshold, the times of occurrence areassigned as the times of activation of the myocardium.

In a further disclosed embodiment, when the local unipolar minimumderivatives are less than a preset unipolar derivative threshold, thetimes of occurrence are assigned as the times of activation of themyocardium.

Typically, classifying the unipolar signal includes defining a pluralityof preset classifications for the unipolar signal. The plurality mayinclude a first classification wherein the number is zero, a secondclassification wherein the number is one, a third classification whereinthe number is two or three, and a fourth classification wherein thenumber is greater than three.

In an alternative embodiment the method includes only assigning a giventime of occurrence as a given time of activation of the myocardium whena confidence level associated with the given time of occurrence isgreater than a preset confidence level. In some embodiments the giventime of occurrence may only be assigned as the given time of activationof the myocardium when an amplitude of a corresponding bipolar signal isgreater than a preset bipolar signal threshold.

There is further provided, according to an embodiment of the presentinvention apparatus, including:

a pair of electrodes configured to be placed in proximity to amyocardium of a human subject; and

a processor configured to:

receive a bipolar signal from the pair of electrodes,

receive a unipolar signal from a selected one of the pair of electrodes,

delineate a window of interest (WOI) for the unipolar and bipolarsignals,

within the WOI compute local unipolar minimum derivatives of theunipolar signal, and times of occurrence of the local unipolar minimumderivatives,

within the WOI compute bipolar derivatives of the bipolar signal at thetimes of occurrence,

evaluate ratios of the bipolar derivatives to the local unipolar minimumderivatives,

when the ratios are greater than a preset threshold ratio value, assignthe times of occurrence as times of activation of the myocardium,

count a number of the times of activation; and

classify the unipolar signal according to the number.

The present disclosure will be more fully understood from the followingdetailed description of the embodiments thereof, taken together with thedrawings, in which:

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic block diagram of an algorithm, according to anembodiment of the present invention;

FIG. 2 is an example of activity as measured by a bipolar signal and aunipolar positive electrode signal, according to an embodiment of thepresent invention;

FIG. 3 is a graph illustrating baseline wander removal, according to anembodiment of the present invention;

FIG. 4 is a block diagram of a baseline wander removal system, accordingto an embodiment of the present invention;

FIG. 5 is a graph of two Gaussian filters, according to an embodiment ofthe present invention;

FIG. 6 is a schematic block diagram of an Annotation Detector blockaccording to an embodiment of the present invention;

FIG. 7 is a graph of unipolar and bipolar signals, and theirderivatives, according to an embodiment of the present invention;

FIG. 8 shows graphs illustrating a first rejection phase of anannotation algorithm, according to an embodiment of the presentinvention;

FIG. 9 shows graphs illustrating local and far field candidateannotations, according to an embodiment of the present invention;

FIG. 10 is a graph illustrating merging of candidate annotations, andhow rejection criteria are used, according to an embodiment of thepresent invention;

FIG. 11 is a graph of a unipolar derivative fuzzy function, according toan embodiment of the present invention;

FIG. 12 is a graph illustrating unipolar signal segmentation, accordingto an embodiment of the present invention;

FIG. 13 is a graph of a unipolar duration fuzzy function, according toan embodiment of the present invention;

FIG. 14 is a graph of a unipolar amplitude fuzzy function, according toan embodiment of the present invention;

FIG. 15 is a graph of a unipolar duration to amplitude ratio fuzzyfunction, according to an embodiment of the present invention;

FIG. 16 is a graph of a bipolar amplitude fuzzy function, according toan embodiment of the present invention;

FIG. 17 is a schematic block diagram of a classification algorithm,according to an embodiment of the present invention;

FIG. 18 is a flowchart showing steps of the classification algorithm,according to an embodiment of the present invention;

FIG. 19 illustrates a single event classification, according to anembodiment of the present invention;

FIG. 20 illustrates the effect of annotation times on theclassification, according to an embodiment of the present invention;

FIG. 21 illustrates a split classification, according to an embodimentof the present invention;

FIG. 22 illustrates a multi classification, according to an embodimentof the present invention; and

FIG. 23 is a schematic illustration of an invasive medical procedureusing an apparatus, according to an embodiment of the present invention.

DETAILED DESCRIPTION EMBODIMENTS

Overview

Embodiments of the present invention use a wavefront annotationalgorithm which acts to combine the properties of two types of ECGsignals—a bipolar signal together with one of its associated unipolarsignals—to generate accurate signal annotations. The inventors haveverified that the algorithm provides accurate annotations which areimmune to far field interferences.

The wavefront annotation algorithm provides automatic and reliabledetection of annotation points that enable acquisition and annotation ofnumerous LAT points in a relatively short time. This abundance of LATpoints makes it difficult and time consuming for the user to inspecteach and every one of those Intra-cardiac signals for additionalimportant clinical information that may be embedded in the signals.

Consequently, embodiments of the present invention use a classificationalgorithm wherein results derived from the wavefront annotationalgorithm automatically identify key signals which may be of addedclinical importance. Specifically, the automatic identification by theclassification algorithm separates between no activation events, singleactivation events and multiple activation events, and within themultiple activation events separates between points with a large numberof activations and those points with only a few activations.

A classification algorithm classifies ECG signals according to theircomplexity. This algorithm measures complexity using enumeration—bycounting the number of detected valid activations within a given timewindow. The inputs to the algorithm are wavefront detected activationsfrom a wavefront annotation algorithm, and a time segment (WOI) forwhich the complexity calculation is required. The output of theclassification algorithm is the classification of an ECG signal. Thealgorithm classification types are No-Lat, Single, Split and Multi.

An embodiment of the present invention provides a method comprisingreceiving a bipolar signal from a pair of electrodes in proximity to amyocardium of a human subject, and receiving a unipolar signal from aselected one of the pair of electrodes. A window of interest (WOI) isdelineated for the unipolar and bipolar signals, and within the WOIlocal unipolar minimum derivatives of the unipolar signal and times ofoccurrence of the local unipolar minimum derivatives are computed.

In addition, within the WOI bipolar derivatives of the bipolar signal atthe times of occurrence are computed and ratios of the bipolarderivatives to the local unipolar minimum derivatives are evaluated.When the ratios are greater than a preset threshold ratio value, thetimes of occurrence are assigned as times of activation of themyocardium, a number of the times of activation is counted, and theunipolar signal is classified according to the number.

Description of Embodiments

The following description is divided into two sections. A first sectiondescribes the wavefront annotation algorithm. A second section describesthe classification algorithm.

1. Wavefront Annotation Algorithm

FIG. 1 is a schematic block diagram of a wavefront annotation algorithm,according to an embodiment of the present invention. The algorithminputs consist of a single bipolar signal and one of its unipolarsignals, which are typically provided to a processor 20 operating thealgorithm, following a low pass filter with a cut-off of 500 Hz and apower rejection filter. More detail of the operation of processor 20 isprovided with reference to FIG. 23 below. The polarity of the unipolarsignal is assumed to be known (i.e. it is derived from either a positiveor a negative electrode). The processor may be a stand-alone processor,and/or a general purpose processor that is typically operating acomputer. The algorithm comprises a number of stages, summarized here.

A pre-processing stage 22 includes removal of baseline wander, low passfiltering and any order of differentiation. The removal of baselinewander includes removal of an additive low frequency signal that is anartifact and originates from various reasons such as mechanical cathetermovement or respiration. This low frequency signal can alter theestimated derivative of the signals and therefore is typically removed.

A feature extraction stage 24 uses the post-processed signals andextracts features for every candidate annotation.

A first annotation detector stage 26 performs eliminations of candidateannotations based on a subset of features.

Next, in a pair elimination stage 28 candidate annotations that pass therequired feature threshold, but are insignificant relative to anothervery close activation may be discarded.

Finally, in a second annotation detector stage 30 a score is given toeach candidate annotation based on its feature values. Only candidateannotations that surpass the score thresholds are considered as validannotations, and the timing and features of these are used by theprocessor in further operations of the processor, such as generating amap of the candidate annotations.

The elements of the algorithm are described in more detail below.

The core of the algorithm relies on three basic observations:

-   -   Unipolar activity is marked by a sharp downward deflection in        the signal amplitude. These deflections can be easily identified        as local minima in the activity velocity signal (i.e. the        derivative of the unipolar signal). However, not all local        minima are indicative for activity; some are results of noise or        far field activity. Therefore, the purpose of the algorithm is        to distinguish between local minima in the velocity signal that        are correlated with local real activations and those that are        not. This is possible using the next observation.    -   Far field activations affect the potential of the unipolar pair        almost identically. Therefore, their bipolar counterpart has        only residual activity during far field activation. This is not        the case during local activity measurements when one of the        electrodes is in proximity with the activation and the other is        relatively far (even a few mm is sufficient). In this case the        bipolar signal will also exhibit slope change concurrent with        the slope change in the unipolar signal (see FIG. 2 and its        description below). Using this phenomenon it is possible to        distinguish between unipolar sharp deflections arising from near        field activations and those arising from far field activations.    -   Combining multiple features of candidate annotations provides a        method for making a more robust annotation detection. For        example, bipolar amplitude change as a result of local        activation may be used as a feature. However, sometimes a far        field activation may have a bipolar component (for example when        mapping inferior atria near the ventricle valve). Therefore,        decision making that is based solely on the bipolar amplitude        may fail. However, if additional features of the signals are        used the decision making may be more robust. One such feature        can be the unipolar amplitude around the activation (FIG. 2 ).

FIG. 2 is an example of activity as measured by the bipolar signal andthe unipolar positive electrode signal, according to an embodiment ofthe present invention. A graph 40 shows the bipolar signal; a graph 44shows the unipolar signal. The sharp downward deflection on the left, ina region “A”, is a near field activity which is concurrent in theunipolar and the bipolar signals. As shown in a region “B” during farfield ventricular activation the unipolar signal changes, however, thebipolar activity is negligible. Embodiments of the present invention usemultiple features of the signal similar to those exemplified above toassist in separating between local and far field activations. Forexample, in region A the unipolar amplitude and its rate are similar tothe bipolar signal, while in region B the unipolar signal amplitude ismuch larger and its rate is much faster than the bipolar signal.

The following description describes the elements of the algorithmillustrated in FIG. 1 .

Pre-Processing and Feature Extraction Stages 22 and 24 (FIG. 1 )

The purpose of these pre-processing and feature extraction stages is toremove and attenuate interferences in the unipolar and bipolar signalswhile maintaining and emphasizing those features of the signal that areused in subsequent stages. While for simplicity the actions describedherein are assumed to occur in stages 22 and 24, it will be understoodthat at least some of these actions may occur in other stages of thealgorithm. A characteristic that we want to retain is the morphology ofactivations, since it reflects slope changes. Characteristics that aretypically discarded are the baseline-wander that acts as an additivesignal that can corrupt the slope measurements and also high frequencynoise. Stages 22 and 24 are divided into four sub-stages:

1. Unipolar Pre-Processing Sub-Stage

The Unipolar pre-processing stage consists of applying the followingsteps in series:

-   -   1. Baseline estimation and subtraction (using a median filter+a        low pass filter (LPF)) at 1 KHz    -   2. Upsampling to 8 KHz (Sample and hold or other upsampling        technique by factor of 8)    -   3. First Smoothing Filter—LPF FIR (−6 db@155 Hz, 145 Taps). The        filter is a convolution of a 65 taps Equiripple filter of 500 Hz        and Gaussian 10 ms window.    -   4. Second Smoothing Filter—The filter used is an antialiasing        LPF of the system, typically a 500 Hz low pass filter.    -   5. Derivative

The derivative of step 5 is used as an input to a unipolar annotationdetector-(Phase I) in first annotation detector stage 26 (FIG. 1 ). Theadditional filtered signal output of step 4 is used for featureextraction stage 24 of the algorithm.

2. Bipolar Pre-Processing Sub-Stage

The bipolar pre-processing stage consists of applying the followingsteps in series:

-   -   1. Baseline estimation and subtraction (median filter+LPF) at 1        KHz    -   2. Upsampling to 8 KHz (Sample and hold by factor of 8)    -   3. Smoothing—LPF FIR (−6 db@310 Hz, 113 Taps). The filter is a        convolution of a 65 taps Equiripple filter of 500 Hz and        Gaussian 6 ms window.    -   4. Derivative

The final output of the bipolar preprocessing stage (the bipolarderivative) is used as an input to the unipolar annotationdetector-(Phase I) referred to above (FIG. 1 ).

3. Baseline Wander Estimation Sub-Stage

Intra-cardiac (IC) signals may contain additive baseline wander signalsarising from movement of the catheter, movement of the subject andrespiration that changes the interface with the tissue (see FIG. 3 andits description below). These motion artifacts contain mostly lowfrequency components. However, the near field activity signal may alsocontain significant energy in these spectral bands. Therefore theconventional approach of removal by high pass IIR or FIR filter isproblematic and can cause distortion and morphology changes to the ICsignals. Consequently, the selected approach that we use is based onestimation of the baseline wander (FIG. 3 ) and its subtraction from thesignal.

FIG. 3 is a graph illustrating baseline wander removal, according to anembodiment of the present invention. A unipolar signal 50 is originallycontaminated by a low frequency artifact, contributing to the baselinewander. The purpose of the baseline estimation is to calculate thebaseline which is then subtracted from the signal. In the figure acalculated baseline 54 has been overlaid on the unipolar signal.Baseline wander rejection is important since baseline wander can addnoise to the estimate of the unipolar derivatives, and thus may affectthe annotation detection.

The estimation of the baseline wander, and its subtraction from theoriginal, is accomplished by removal of the near field activity using aseries of two filters as is illustrated in FIG. 4 .

FIG. 4 is a block diagram of a baseline wander removal system, accordingto an embodiment of the present invention. A median filter 60, typicallyhaving a window of 60 ms, is designed to remove the activities from theraw signal while an LPF 64, which in one embodiment is an 89 taps FIRHanning filter with a typical cut-off of approximately 10 Hz, isdesigned to smooth out edges resulting from the median filter. Finallythe baseline estimate is subtracted from the raw signal, by a process ofnegation 68 then summation 72, resulting in a signal free of baselinewander.

4. Smoothed Derivative Sub-Stage

FIG. 5 is a graph of two Gaussian filters, according to an embodiment ofthe present invention. The detection of sharp deflection points in thesignal is based on the velocity of the signal, therefore a derivativeapproach is used. However, derivative functions act as a high passfilter, thus enhancing high frequency noise. Therefore, we use asmoothing function to decrease the noise in the derivative estimation.The smoothing function that we use are normalized zero mean Gaussianfunctions, comprising a unipolar Gaussian function 80 and a bipolarGaussian function 84, illustrated in FIG. 5 . These unipolar and bipolarGaussian filters have 90% of the energy in time windows of ±2 ms and ±1ms respectively. Thus activations or approaching far fields at distanceslarger than these values are virtually ignored and do not affect thederivative value.

Annotation Detector-I Stage 26 (FIG. 1 )

Reference is now made to FIGS. 6-9 . FIG. 6 is a schematic block diagramof Annotation Detector-I stage 26; FIG. 7 is a graph of unipolar andbipolar signals, and their derivatives; FIG. 8 has graphs illustrating afirst rejection phase of the annotation algorithm; and FIG. 9 has graphsillustrating local and far field candidate annotations, according toembodiments of the present invention.

Referring to FIG. 6 , Table I below gives parameters used in thedetector, and corresponding acronyms in the block diagram.

TABLE I Parameter Acronym Smoothed Unipolar Derivative S-Uni SmoothedUnipolar Derivative Threshold Th-Uni Smoothed Bipolar Derivative S-BipSmoothed Bipolar Derivative Threshold Th-Bip$\frac{S - {Bip}}{S - {Uni}}$ R The minimal ratio that R should exceedin order to be a Th-Ratio valid annotation at the output of annotationdetector-I

FIG. 7 shows an example of a bipolar slope of zero around a unipolarannotation. The graphs show a unipolar distal signal 100, its derivative102, its local activation (A) as well as a bipolar signal 104, and itsderivative 106. Notice that at the unipolar deflection point (A) thebipolar derivative is almost zero and it is not indicative of the largechange in bipolar amplitude.

FIG. 8 has graphs illustrating a first rejection stage of the annotationalgorithm of FIG. 1 . A top graph 110 shows the unipolar signal and abottom graph 114 shows its smoothed derivative. Black dots 118 areminima values in the derivative signal below a threshold value and willbe further considered as possible annotation points while grey dots 122mark minima value above the threshold that will be rejected.

FIG. 9 illustrates separation between local (A) and far field (B)candidate annotations using the bipolar and unipolar derivative ratiofeature described herein. The figure shows unipolar 130 and bipolar 132signals, and unipolar 136 and bipolar 138 derivatives. In localactivation, unipolar derivative changes are accompanied by a bipolarderivative change as illustrated by a 2 ms activity window 140. However,this is not the case in the far field derived deflection (B), asillustrated by window 144, thus the ratio between the change in thebipolar and unipolar slope for the far field case will be below therequired ratio threshold.

Returning to FIG. 6 , the inputs for the annotation detector-I block arethe relevant unipolar signal derivative under test, its polarity and itssmoothed bipolar derivative. The outputs of the block are the annotationindexes and their slope value (the unipolar derivative value at theannotation index). The slope value acts as the score of the annotation.

In an embodiment of the invention the deflection points in thedownslopes of the unipolar signal are detected, in blocks 90 and 92, byfinding the minima points below a threshold (typically −0.01 mv/ms), seealso FIG. 8 . Activities typically satisfy this condition in addition totwo others:

-   -   1. The value of the bipolar smoothed derivative signal (S-BIP)        in a time window around the unipolar deflection points        (typically ±2 ms) should exceed in a negative manner, a        threshold TH-BIP. Thus, S-BIP<TH-BIP. In one embodiment TH-BIP        is typically about 0.008 mv/ms.    -   2. The ratio between this bipolar smoothed derivative value and        the unipolar smoothed derivative slope value should be higher        than Th-Ratio, typically about 0.2.

#1 and #2 are evaluated in blocks 94 and 96, and in a decision 98.

Referring to FIG. 6 , the bipolar derivative value (S-bip) is computeddifferently for positive and negative electrodes. In a disclosedembodiment, for a positive electrode it is the minimal value within a 2ms time window, and for a negative electrode it is the negative value ofthe maximal value within that time window. The reason for using a timewindow and not the derivative at the annotation point is that in certainpathologies and/or orientations (of the catheter and the wavepropagation direction) the bipolar signal at a given point can be smallor even zero since the time delay of activities between unipolaractivations can cancel out (FIG. 7 ). The value is calculateddifferently for positive and negative electrodes since the tip activityat the positive electrode is registered as a downslope in the bipolarsignal, while activity at the negative electrode is registered as anupslope in the bipolar signal.

The ratio between the unipolar and the bipolar derivatives may also beused as a classification criterion since this criterion can distinguishbetween near field and far field activity. In near field activity atleast some of the downslope activity is typically represented in thebipolar signal, while in far field cases the bipolar signal may onlyhave residual activity.

Pair Elimination Stage 28 (FIG. 1 )

The pair elimination stage of the algorithm is responsible for mergingtwo annotations that arise from a single activity. This split phenomenacan occur when for some reason the downward slope of a near fieldactivity contains a momentary upslope, either from activity recorded inthe other electrode or from far field activity that influences oneelectrode more than the other. The momentary upslope will cause twominima in the derivative of the signal, and if these are strong enoughthey result in two annotations. In order to exclude these cases weevaluate the change in the signal due to the upslope.

All annotation pairs in the same unipolar signal that are not too farapart (typically less than 50 ms) are analyzed for a split. The segmentbetween the two candidate annotations in the unipolar derivative signalis analyzed for upsloping. When the upsloping amplitude is consideredsignificant the two annotations are maintained. If not, the annotationwith a smaller downslope is discarded.

FIG. 10 is a graph 150 illustrating merging of candidate annotations,and how rejection criteria are used, according to an embodiment of thepresent invention. The graph shows a unipolar derivative signal and twopossible annotations (circles, marked A[i] and A[i+1]).

The purpose of pair elimination block 38 is to decide whether theupsloping amplitude change (marked with a vertical double-headed arrow)between the smallest derivative amplitude and the peak P between the twopossible annotations is significant or not. If the change is consideredsignificant both annotations are maintained, otherwise the weakeractivation—A[i] is discarded.

Thus, for an annotation A[i] to be discarded the relative change to thepeak amplitude (P) between any adjacent candidates annotation with astronger slope within will not be rejected. In the 50 ms time windowsA[i+1] is considered. If the peak is significantly higher this pointmathematical terms, in one embodiment, if the value of(P−A[i])/(0.02−A[i]) is lower than 0.5 the annotation A[i] is discarded.I.e., annotation A[i] is rejected if one or more annotations in the 50ms time window follow the above rule.

Annotation Detector II Stage 30 (FIG. 1 )

The candidate annotations that passed the earlier phases are revaluatedin this block using additional features and metrics. Only annotationsthat pass this block and that also pass a user bipolar voltagecontrolled threshold are considered valid annotations. For eachannotation multiple features are computed. Each feature value is given afuzzy score ranging from zero to one, corresponding to a confidencevalue for the feature. Finally, all scores are combined together andtheir value is tested against a global score threshold. Thoseannotations that pass the global score threshold, i.e., that have a highconfidence value, are considered valid annotations and those that donot, i.e., that have a low confidence value, are rejected.

The fuzzy functions described herein are examples of such functions thatare used in one embodiment of the present invention. However, other suchfuzzy functions or other probabilistic terms/functions will be apparentto those having ordinary skill in the art, and all such functions areassumed to be included within the scope of the present invention. Inaddition, for a specific requirement multiple fuzzy scores may be used(for example—fuzzy functions that highlight strong or small bipolarsignals etc.)

All fuzzy functions are bounded between 0 and 1.

The features that the block uses are:

-   -   1. The unipolar derivative value    -   2. The duration, s₂, of the unipolar slope    -   3. The amplitude of the unipolar slope at that time window, s₂    -   4. The ratio between the above duration and amplitude    -   5. The bipolar signal amplitude in the time window—s₂

The five features are explained below.

1. Unipolar Derivative

FIG. 11 is a graph 160 of a unipolar derivative fuzzy function,according to an embodiment of the present invention. The graph providesa score f(s₁) assigned to the derivative, where the derivative value isherein termed s₁. As shown in the graph, values of the derivative below−0.07 receive a score of 1, and values larger than −0.07 decreaselinearly such that a 0.5 score is reached at a slope of −0.018.Derivative values smaller than −0.01 receive a score of zero.

The unipolar derivative s₁ is used in both detector stages, but unlikethe first stage where it has a dichotomy threshold of 0.01 mv/ms, hereits value is used to provide the score f(s₁). The higher the score themore probable that this is a valid annotation according to this featurealone.

2. Unipolar Activity Segmentation and Duration

FIG. 12 shows graphs illustrating unipolar signal segmentation,according to an embodiment of the present invention. The segmentation isdescribed further below. A unipolar signal 170 and its derivative 174are illustrated around a candidate annotation time index 176 (blackdot). A dotted horizontal line 180 representing a threshold marks asearch segment (typically approximately ±25 ms) in both directions. Inone embodiment the segment value is defined as 20% of the absolutemaximum unipolar derivative value at the annotation point. Segments A, Bmark the time intervals within the search window where the signalderivative is below the threshold. A final segment in this example canbe either segment A or, if certain conditions (described hereinbelow)are met, it can be the joint segment starting from onset of A to the endof B.

A feature that we derive from the unipolar signal is the duration s₂ ofthe downslope segment around the candidate annotation. The aim is todetect the unipolar downslope from its initial descent until it startsto upslope. The motivation is to inspect features of the signals in thatsegment, such as properties of duration, amplitude, and theirrelationship, and to use them as a basis for a classifier. The inventorsconsidered several methods for this task, all of which worked well forthe obvious cases of a single slope, but the method described herein wasselected since it works well on complicated cases having slope trendchanges and local peaks within the slope segment.

Referring to FIG. 12 , the segmentation is based on analyzing theunipolar derivative via the following steps:

-   -   1. A 50 ms segment of unipolar signal derivative 174 centered on        the candidate annotation time index 176 is considered as the        maximum span on threshold line 180 for which the segment can be        defined. In FIG. 12 the span is between end points 182 and 186        of line 180. We assume that the unipolar signal down slope        segment is bounded in this 50 ms time window. If the segment is        larger than this it is force-bounded to this 50 ms interval.    -   2. The derivative segment amplitude is compared against a        constant threshold. The threshold in the embodiment described        herein is assumed to be 20% of the unipolar derivative value at        the candidate annotation time. Segments on line 180 that are        below that value are marked in FIG. 12 as two segments A and B.    -   3. The next step is to compute the segment bounds and to sum the        area under the derivative at each sub-segment separately,        corresponding to summing the absolute value of the signal in        those segments.    -   4. Segments merge—Based on the segments interspacing and their        area a decision is taken whether the final segment should        contain the main segment (A) or additional segments (B). In one        embodiment, in order for segments to join adjacent endpoints        must be 1 ms or less from each other and the additional        segment (B) should have an area less than 30% of the main        segment, so that the signal delta for B should be less than 30%        of the signal delta for A.

The duration determined from the above steps, herein termed s₂, is thenassigned a score f(s₂) using the fuzzy function described below withreference to FIG. 13 .

FIG. 13 is a graph 190 of a unipolar duration fuzzy function, accordingto an embodiment of the present invention. Very short slopes of lessthan 2 ms are unlikely to originate from real activation; very longactivations are probably far field events. In addition, the unipolarduration for local valid activation cannot be too short and cannot betoo long. The above observations are encapsulated in the fuzzy functionof FIG. 13 , which provides the score f(s₂). The function points 192,194 are: {2,0.5}.{19,0.5} and the slopes are 0.5 and −0.5 respectively.

3. Unipolar Amplitude

FIG. 14 is a graph 200 of a unipolar amplitude fuzzy function, accordingto an embodiment of the present invention. The unipolar amplitude is theamplitude of the unipolar signal (herein termed s₃) in the detectedactivity segment (peak-to-peak) duration s₂. In one embodiment the fuzzyfunction slope intersects points 202, 204: {0.1,0.5},{0.42,1}. The scorederived from the fuzzy function, f(s₃), is high the higher the amplitudeof the signal. I.e., for high scores, and high amplitudes, the morelikely it is that the signal originates from a local activation, unlessthe far field signals have a large amplitude.

4. Unipolar Duration to Amplitude Ratio

FIG. 15 is a graph 210 of a unipolar duration to amplitude ratio fuzzyfunction, according to an embodiment of the present invention. Theunipolar duration to amplitude ratio excludes high ratio values sincethe longer the activity and the smaller the amplitude, the more likelythat this is a false annotation. In one embodiment the equation of thefuzzy function line isf(s ₄)=−0.0184·s ₄+1.283  (1)where s₄ is the duration to amplitude ratio, and

-   f(s₄) is the score assigned to the ratio.    5. Bipolar Amplitude

FIG. 16 is a graph 220 of a bipolar amplitude fuzzy function, accordingto an embodiment of the present invention. The bipolar amplitude withinthe unipolar activity segment (peak-to-peak), s₂, is also used forscoring the likelihood of the candidate annotations. The higher thevalue, the more likely that this is a true activation.

An equation for the fuzzy function is:f(s ₅)=25·s ₅, 0≤s ₅≤0.04; f(s ₅)=1, s ₅>0.04  (2)where s₅ is the bipolar amplitude, and

-   f(s₅) is the score assigned to the amplitude.

The amplitude is calculated on the baseline rejected bipolar smoothedsignal after low pass of Gaussian and anti-aliasing filter.

6. Final Score

As described above, each feature receives a score and the scores areused together in generating a global score. The idea is that featurescan support one another in inclusion or exclusion of an annotation. Inone embodiment the score method which we used is defined as follows:

$\begin{matrix}{{GS} = \sqrt[5]{\prod\limits_{1}^{5}\;{f\left( s_{i} \right)}}} & (3)\end{matrix}$

where GS is the global score.

The value of GS should pass a specific threshold, for example 0.8, forthe annotation to be considered as valid.

It will be apparent to those skilled in the art that global scores,different from those exemplified above but having an equivalent outcome,can be used in embodiments of the present invention. Such global scorescan include substantially any combination of weighted average ofindividual scores, and/or dot products of individual scores. Such globalscores can also include a composition of scores based on a subset offuzzy features. The scope of the present invention includes all suchglobal scores.

Bipolar Amplitude Filtering

In some embodiments a final stage of the algorithm is designed toprovide the user the ability to eliminate annotations that were detectedif they have a low bipolar amplitude. The required amplitude thresholdis controlled by the user. The bipolar amplitude filtering compares thebipolar amplitude of each annotation that surpassed the post processingstage with a threshold. Only annotations having a bipolar amplitude thatexceeds the threshold are passed to the system. (If a user desires toskip this stage she/he may set the threshold to zero, thus eliminatingthe rule of this stage.)

The bipolar amplitude of each annotation is defined by measuring thepeak-to-peak amplitude, baseline removed, 1 KHz bipolar signal in a 14ms window centered around the annotation time (maximum unipolar velocitypoint). In one embodiment a system default value of bipolar amplitudethreshold is set to 30 micro Volts.

This bipolar amplitude is different from the fuzzy controlled bipolaramplitude (described above), since this bipolar amplitude is determinedon a fixed interval. The fuzzy classifier uses a dynamic segment of theunipolar activation and therefore in some embodiments the dynamicsegment may be more meaningful as a classifier. In addition thisclassifier is used as a dichotomic user controlled threshold.

Algorithm Final Output

All annotations that pass the fuzzy score and the bipolar usercontrolled bipolar amplitude are considered valid annotations that maybe used by the processor.

In one embodiment each annotation should have the following features:

-   -   1. The annotation time index    -   2. The unipolar and bipolar derivative value    -   3. The fuzzy score    -   4. The unipolar detected downslope segment duration    -   5. The unipolar amplitude within that segment    -   6. The bipolar amplitude within that segment    -   7. The bipolar amplitude for the user controlled value

In addition trace files may be provided, to include

-   -   1. The specific fuzzy score for each of the features    -   2. The unipolar segment start and end time index

2. Classification Algorithm

FIG. 17 is a schematic block diagram of a classification algorithm,according to an embodiment of the present invention. The algorithminputs consist of the ECG signal's annotation data that is derived fromthe wavefront annotation algorithm final output (the outputs are listedabove), and a window of interest (WOI). The WOI may be any convenienttime segment that includes the ECG signal, and is selected by thealgorithm user, typically based on such factors as the tachycardia typeand the mapping type. Additional inputs to the algorithm are describedbelow with reference to FIG. 18 .

A wavefront annotation is the position in time where the absolute valueof the local unipolar signal slope is a minimum.

The data associated with each wavefront annotation contains the LAT(local activation time), local unipolar and bipolar slope (dv/dt) and ascore value on the range 0-1, respectively corresponding to items 1, 2,and 3 of the wavefront annotation algorithm final output section. Thescore attribute establishes the likelihood of the annotation point beinga correct annotation point. These attributes typically exist only inelectro-anatomical activations that are detected using the wavefrontalgorithm.

FIG. 17 illustrates that the classification algorithm applies, in alogic block 230, a number of activations and a time difference betweenthe activations as illustrated by respective blocks 232 and 234. Actionsperformed in the logic block are described below with reference to FIG.18 .

FIG. 18 is a flowchart showing steps of the classification algorithmperformed in logic block 230, according to an embodiment of the presentinvention. In addition to the inputs described above, additional inputsto the algorithm are shown in FIG. 18 . These inputs are:

1. A minimum required bipolar amplitude. Each annotation point mustexceed a threshold value (Bip-Th) to be considered as a valid point forthe enumeration.

2. A minimum required likelihood score. Each annotation point scorevalue must exceed a threshold (Fuz-Th) value in order to be consideredas a valid point for the enumeration process. The score value may bedifferent for each mapping chamber (Ventricular/Atria).

3. A minimum required activation time, min interval, for complexclassification. This value is used when two or more valid activationsexist in the WOI. If the time interval between the earliest to thelatest activation is smaller than this threshold value the activationwill be classified as a single event, otherwise it will be classified aseither split or multi depending on the number of activations within theWOI.

While the description above assumes one value for the min interval,embodiments of the present invention include the option of selectingdifferent values of min interval for different anatomic regionsoriginating the ECG signal. In addition, the WOI may dynamically change.Also, the ECG signals analyzed may be limited to those either below orabove a specific bipolar amplitude, or to those that have mixedamplitudes (at least one above some value and at least another belowsome other value).

In a selection block 240, only annotations within the WOI, and where thefollowing expression is true, are selected:Bip(n)>Bip-Th AND Fuz(n)>Fuz-Th

The selected annotations are counted in a counting block 242, to give anumber N, and the group of selected annotations are then classified infour comparisons 244, 246, 248, and 250.

Comparison 244 checks if N=0, in which case the group is classified asNo-Lat in a first classification block 252.

Comparison 246 checks if N=1, in which case the group is classified as“Single” in a second classification block 254. If comparison 246 returnsnegative, a further calculation of a maximum LAT difference DT is madein a calculation block 260, and if, in comparison 248 DT<min interval,the annotations are treated as being one annotation, and the group isalso classified as Single in block 254.

If comparison 248 returns negative there are two or more assumedannotations, and these are classified in comparison 252, which checks ifN>3. If comparison 252 returns positive, the group is classified asMulti in a third classification block 256. If comparison 252 returnsnegative, in the case of N=2 or N=3, the group is classified as Split ina fourth classification block 258.

The classification results may typically be presented to the algorithmuser on a display screen, such as display screen 450 referred to in thedescription of FIG. 23 below.

EXAMPLES

We present below several examples of unipolar and bipolar signals alongwith the annotations input and the WOI input. Each example includes anexplanation of the expected complex point classification. While theexamples described have two unipolar signals, a distal and a proximalsignal, it will be understood that the algorithm described herein onlyrequires one unipolar signal.

FIG. 19 illustrates a single event classification, according to anembodiment of the present invention. The figure shows separate distaland proximal unipolar signals with the input annotations to theclassification algorithm marked. A rectangle 270 marks the window ofinterest (WOI) wherein the algorithm operates. Other annotations outsidethe WOI do not contribute to the classification.

Both the distal and the proximal electrodes have a single annotationevent in the region of interest, thus resulting in a classification of“single event” for each of the electrodes.

FIG. 20 illustrates the effect of annotation times on theclassification, according to an embodiment of the present invention. Asfor FIG. 19 , a rectangle 280 marks the window of interest (WOI) whereinthe algorithm operates. Other annotations outside the WOI do notcontribute to the classification.

The distal annotations have two consecutive events that are very closetime wise and that are marked in the WOI with two points 284, thusresulting in a classification of “single event”. In this case the flowthrough the flowchart is N=1? No; Calculate Maximum LAT Difference (DT);DT<min interval? Yes.

The proximal annotations, points 288, are further apart, thus resultingin classification of “split event”. In this case the flow through theflowchart is N=1? in comparison 246 No; Calculate Maximum LAT Difference(DT) in block 260; DT<min interval? in comparison 248 No; N>3? Incomparison 250 No.

FIG. 21 illustrates a split classification, according to an embodimentof the present invention. For this example, the WOI is assumed to coverthe entire signal shown. In this case both electrodes will be classifiedhere as split, since the time difference (for each electrode separately)between the earliest and latest activity is longer than the “mininterval” parameter of the flowchart (FIG. 18 ). The classification tosplit is because the number of annotations in the distal electrode isthree and for the proximal electrode it is two respectively. In bothcases the flowchart arrives to N>3?, and in both cases the answer, beingfewer than 4 annotations, is No.

FIG. 22 illustrates a multi classification, according to an embodimentof the present invention. As for FIG. 21 the WOI is assumed to cover theentire signal shown. In this case the distal electrode will beclassified as “Multi” since the number of annotations is larger than 3and since the time difference between the earliest and latest activityis longer than the “min interval” parameter of the flowchart. TheProximal electrode in this case will be classified as a single event.

The final classification can further be based on a check of consistencybetween consecutive beats. When considering any specific beat in a WOIthe previous several beats (typically one or two previous beats) can beused. Each previous beat receives individually its classification asexplained previously, relative to its WOI, while the finalclassification of a current beat will be based on majority voting, wherethe different classifications may be assigned relative weights. Forexample, single may have a higher weight than multi, which in turn has ahigher weight than split. In this case, if the current beat isclassified as split but the previous one is classified as single thenthe final classification of the current beat will not be split butrather single. While this is the most basic form of consistency,additional consistency measures apparent to those having ordinary skillin the art may include morphology matching of unipolar or bipolarsignals or time events analysis between beats. All such consistencymeasures are included within the scope of the present invention.

FIG. 23 is a schematic illustration of an invasive medical procedureusing an apparatus 400, according to an embodiment of the presentinvention. The procedure is performed by a medical professional 402,and, by way of example, the procedure in the description hereinbelow isassumed to comprise acquisition of ECG signals from a heart 404 of ahuman patient 406.

In order to acquire the signals, professional 402 inserts a probe 408into a sheath 410 that has been pre-positioned in a lumen of thepatient. Sheath 410 is positioned so that a distal end 412 of the probemay enter the heart of the patient, after exiting a distal end 414 ofthe sheath, and contact tissue of the heart.

Probe 408 may comprise any type of catheter that can be inserted intothe heart of the patient, and that can be tracked, typically using amagnetic tracking system and/or an impedance measuring system. Forexample, probe 408 may comprise a lasso catheter, a shaft-like catheter,or a pentaRay catheter, produced by Biosense Webster of Diamond Bar,Calif., or catheters generally similar to these catheters. BiosenseWebster also produces a magnetic tracking system and an impedancemeasuring system that may be used in embodiments of the presentinvention.

Probe 408 comprises at least two electrodes 411, which are used toacquire the ECG signals used by processor 20 in performing thealgorithms described herein.

Apparatus 400 is controlled by processor 20 (FIG. 1 ), and the processormay comprise real-time noise reduction circuitry 420, typicallyconfigured as a field programmable gate array (FPGA), followed by ananalog-to-digital (A/D) signal conversion integrated circuit 424. Theprocessor can pass the signal from ND circuit 424 to another processorand can be programmed to perform the algorithms disclosed herein.

Processor 20 is located in an operating console 430 of the apparatus.Console 430 comprises controls 432 which are used by professional 402 tocommunicate with the processor. During the procedure, processor 20communicates with an ECG module 436 in a module bank 440, in order toacquire ECG signals as well as to perform the algorithms disclosedherein.

ECG module 436 receives ECG signals from electrode 411. In oneembodiment the signals are transferred, in module 436, through a lownoise pre-amplifier 438, and via a band pass filter 440, to a mainamplifier 442. Module 436 also comprises an analog to digital converter(ADC) 444, which transfers digitized values of the ECG signals toprocessor 20, for implementation by the processor of the algorithmsdescribed herein. Typically, processor 20 controls the operation ofpre-amplifier 438, filter 440, amplifier 442, and ADC 444.

Thus, ECG module 436 enables processor 20 to acquire and analyze EP(electrophysiological) signals received by electrode 411, including theECG signals referred to herein. The signals are typically presented toprofessional 402 as voltage-time graphs, which are updated in real time,on a display screen 450.

The software for processor 20 and module bank 440 may be downloaded tothe processor in electronic form, over a network, for example.Alternatively or additionally, the software may be provided onnon-transitory tangible media, such as optical, magnetic, or electronicstorage media.

In order to operate apparatus 12, module bank 50 typically comprisesmodules other than the ECG module described above, such as one or moretracking modules allowing the processor to track the distal end of probe408. For simplicity, such other modules are not illustrated in FIG. 1 .All modules may comprise hardware as well as software elements.

In addition to display screen 450 presenting ECG signals acquired byelectrode 411, results 452 of the algorithms described herein may alsobe presented to the algorithm user on the display screen.

It will be appreciated that the embodiments described above are cited byway of example, and that the present invention is not limited to whathas been particularly shown and described hereinabove. Rather, the scopeof the present invention includes both combinations and subcombinationsof the various features described hereinabove, as well as variations andmodifications thereof which would occur to persons skilled in the artupon reading the foregoing description and which are not disclosed inthe prior art.

What is claimed is:
 1. Apparatus, comprising: a pair of electrodesconfigured to be placed in proximity to a myocardium of a human subject;and a processor configured to: receive a bipolar signal from the pair ofelectrodes, receive a unipolar signal with a unipolar feature from aselected one of the pair of electrodes; delineate a window of interest(WOI) representative of a time interval for the unipolar and bipolarsignals; within the WOI compute local unipolar minimum derivatives ofthe unipolar signal and times of occurrence of the local unipolarminimum derivatives; within the WOI compute bipolar derivatives of thebipolar signal at the times of occurrence; evaluate ratios of thebipolar derivatives to the local unipolar minimum derivatives; evaluatethe bipolar feature and the unipolar feature using at least one fuzzyfunction to provide a score representative of a confidence value,wherein the fuzzy function evaluates a ratio of unipolar duration toamplitude of the unipolar signal, and an amplitude of the bipolarsignal; when the ratios are greater than a preset threshold ratio valueand the score exceeds a preset threshold score value, assign the timesof occurrence as times of activation of the myocardium; count a numberof the times of activation; and classify the unipolar signal as oneselected from the group consisting of a no activation event, a splitevent, a single activation event and a multiple activation event,according to the number; a filter configured to minimize interferencesin the bipolar signal and the unipolar signal prior to computation ofthe local unipolar minimum derivatives and the bipolar derivatives; andwherein in a split event, the processor is configured to eliminate aweaker feature of a pair of features of the unipolar signal whenrespective annotations of the pair of features are within a 50 ms timewindow.
 2. The apparatus of claim 1, wherein the fuzzy function includesa unipolar duration fuzzy function.
 3. The apparatus of claim 1, whereinthe fuzzy function includes a unipolar amplitude fuzzy function.
 4. Theapparatus of claim 1, wherein the fuzzy function includes a unipolarduration to amplitude ratio fuzzy function.
 5. The apparatus of claim 1,wherein the processor is configured to assign the times of occurrence asthe times of activation of the myocardium when the bipolar derivativesare less than a preset bipolar derivative threshold.
 6. The apparatus ofclaim 1, wherein the processor is configured to classify the unipolarsignal comprises defining a plurality of preset classifications for theunipolar signal.
 7. The apparatus of claim 6, wherein the pluralitycomprises a first classification wherein the number is zero, a secondclassification wherein the number is one, a third classification whereinthe number is two or three, and a fourth classification wherein thenumber is greater than three.
 8. The apparatus of claim 1, wherein theprocessor is configured to assign a given time of occurrence as a giventime of activation of the myocardium when a confidence level associatedwith the given time of occurrence is greater than a preset confidencelevel.
 9. The apparatus of claim 8, wherein the processor is configuredto solely assign the given time of occurrence as the given time ofactivation of the myocardium when an amplitude of a correspondingbipolar signal is greater than a preset bipolar signal threshold.